
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


__a         =   logging.get_logger(__name__)  # pylint: disable=invalid-name
class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	def __init__(			self    :     str			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     str=7_6_8			)			->	Dict:
					super().__init__(SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   proj_size

					lowercase_	    =   CLIPVisionModel(SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   PaintByExampleMapper(SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   nn.LayerNorm(config.hidden_size			)
					lowercase_	    =   nn.Linear(config.hidden_size			,				self.proj_size			)

					# uncondition for scaling
					lowercase_	    =   nn.Parameter(torch.randn((1, 1, self.proj_size)			)			)




	def 		_lowercase    (			self    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     int=False			)			->	List[str]:
					lowercase_	    =   self.model(pixel_values=SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   clip_output.pooler_output
					lowercase_	    =   self.mapper(latent_states[:, None]			)
					lowercase_	    =   self.final_layer_norm(SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   self.proj_out(SCREAMING_SNAKE_CASE_			)
					if return_uncond_vector:
									return latent_states, self.uncond_vector

					return latent_states

class 					lowercase__(							nn.Module				):



	"""simple docstring"""




	def __init__(			self    :     str			,				SCREAMING_SNAKE_CASE_    :     Any			)			->	Dict:
					super().__init__()
					lowercase_	    =   (config.num_hidden_layers + 1) // 5
					lowercase_	    =   config.hidden_size
					lowercase_	    =   1
					lowercase_	    =   nn.ModuleList(
					    [
					        BasicTransformerBlock(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				activation_fn='''gelu'''			,				attention_bias=SCREAMING_SNAKE_CASE_			)
					        for _ in range(SCREAMING_SNAKE_CASE_			)
					    ]			)




	def 		_lowercase    (			self    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]			)			->	List[Any]:
					for block in self.blocks:
									lowercase_	    =   block(SCREAMING_SNAKE_CASE_			)

					return hidden_states









'''simple docstring'''






from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream

class 				a__(   lowerCamelCase__      ):







    def __init__( self					:    int		,     __snake_case					:    Callable		,     __snake_case					:    Optional[Features] = None		,     __snake_case					:    str = None		,     __snake_case					:    bool = False		,     __snake_case					:    bool = False		,     __snake_case					:    Optional[dict] = None		,     __snake_case					:    Optional[int] = None		,     **__snake_case					:    Optional[int]		,     ):
      super().__init__(
          features=__snake_case		,     cache_dir=__snake_case		,     keep_in_memory=__snake_case		,     streaming=__snake_case		,     num_proc=__snake_case		,     **__snake_case		,     )
      a		:   List[Any]	  =	Generator(
          cache_dir=__snake_case		,     features=__snake_case		,     generator=__snake_case		,     gen_kwargs=__snake_case		,     **__snake_case		,     )







    def      lowercase_			( self					:    Any      ):
      # Build iterable dataset
      if self.streaming:
        a		:   Optional[int]	  =	self.builder.as_streaming_dataset(split='train'      )
      # Build regular (map-style) dataset
      else:
        a		:   List[Any]	  =	None
        a		:   Any	  =	None
        a		:   Optional[int]	  =	None
        a		:   List[str]	  =	None

        self.builder.download_and_prepare(
            download_config=__snake_case		,     download_mode=__snake_case		,     verification_mode=__snake_case		,     base_path=__snake_case		,     num_proc=self.num_proc		,     )
        a		:   Dict	  =	self.builder.as_dataset(
            split='train'		,     verification_mode=__snake_case		,     in_memory=self.keep_in_memory      )
      return dataset